This repository contains the code for the experiments in the paper "A Predictive Approach to Bayesian Nonparametric Survival Analysis" by Edwin Fong and Brieuc Lehmann.
To install the package, just run the following in the main folder:
python setup.py install
This may not work for newer Macs, in which case we recommend using pip instead in the main folder:
pip install .
We recommend creating a clean virtual environment before doing the above:
python3 -m venv ~/virtualenvs/survival_copula
source ~/virtualenvs/survival_copula/bin/activate
where ~/virtualenvs can be your preferred directory. Please also make sure to have the latest version of setuptools or pip before installing. We have tested the above with pip 21.3 and Python 3.8.9.
Please check the JAX page for CPU versus GPU usage and installation instructions. For the paper results, we use the CPU version for reproducibility as GPU calculations can be non-deterministic, and timing was carried out on the GPU version. For full reproducibility of the experiments in the paper, please use the versions jax==0.2.21 and jaxlib==0.1.71.
The version of R used is 4.1.1 for the MCMC examples. Please install the dirichletprocess package here and the ddpanova package here.
All the main functions are in surv_copula/copula_survival_functions.py for the exponential copula with no covariates, and in surv_copula/copula_survreg_gaussian_functions.py for the lognormal copula with covariates.
Experiment run scripts are kept in the run_expts folder, including notebooks to plot. The scripts are prefixed based on the order in which the experiments appear in the paper, and running the Python scripts should involve entering the following in terminal when in the run_expt folder, for example:
python3 1_sim.py
The R scripts should be run (in Rstudio or terminal) after the respective Python scripts, as some datasets are simulated/split into the run_expt/data folder by the Python scripts.
Outputs from the experiments are stored in run_expt/plot_files, which are then used by the the respective Jupyter notebook with the prefix to produce the plots in the paper and supplementary material. The run_expt/plot_files folder may need to be created for the scripts to run.
We have included the following datasets in run_expts/data for convenience:
- PBC (Dickson et al., 1989)
- Melanoma (Venables & Ripley, 2002)
- Kidney (Klein et al., 2012)
The processing script used is provided in R/process_data.R.
Dickson, E. R., Grambsch, P. M., Fleming, T. R., Fisher, L. D., & Langworthy, A. (1989). Prognosis in primary biliary cirrhosis: model for decision making. Hepatology, 10(1), 1-7.
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (Fourth) [ISBN 0-387-95457-0]. Springer. http://www.stats.ox.ac.uk/pub/MASS4
Klein, Moeschberger, & modifications by Jun Yan. (2012). KMsurv: Data sets from Klein and Moeschberger (1997), Survival analysis [R package version 0.1-5]. https://CRAN.R-project.org/package=KMsurv